What is the purpose of Python slots — especially in terms of when you would want to use it, and when not?
The main advantage of Python slots is that they reduce memory usage. By default, Python objects store attributes in a dictionary (dict). However, when you define slots for a class, Python allocates a fixed size array for storing attributes, thus preventing the creation of a dict for each instance.
If you’re working with many instances of a class that only need a few attributes, using Python slots can drastically reduce memory usage. However, if you need dynamic attribute assignments, slots might not be ideal because they restrict adding new attributes at runtime.
Python slots can offer performance improvements in certain situations. Since attribute lookup is faster (no need to search in a dictionary), it can be helpful in performance-critical applications, such as when you are instantiating a large number of objects with fixed attributes.
However, the trade-off is that slots can make the code less flexible, as you can’t add arbitrary attributes to objects once slots are defined.
You should use Python slots when you know your class will not need to have dynamic attributes and will only contain a predefined set of attributes. For example, in classes designed for lightweight data storage (like tuples or namedtuples), slots are a good choice to optimize memory and performance.
However, if you need to work with classes that require flexibility in attribute management (such as in some ORM systems or dynamic attribute use cases), you may want to avoid slots.